Andreasenjoyce6097

11, 0.02-0.54,

= 0.006) and with thymic carcinoma (HR 2.27, 1.22-4.24,

= 0.01).

resection of thymic tumors with vascular involvement can be performed with optimal surgical results in a high volume center. From the oncological point of view, the involvement of the great vessels seems to be associated with a higher recurrence rate without affecting long-term survival.

resection of thymic tumors with vascular involvement can be performed with optimal surgical results in a high volume center. From the oncological point of view, the involvement of the great vessels seems to be associated with a higher recurrence rate without affecting long-term survival.Renewable energy sources, which are controllable under the management of the microgrids with the contribution of energy storage systems and smart inverters, can support power system frequency regulation along with traditionally frequency control providers. This issue will not be viable without a robust communication architecture that meets all communication specification requirements of frequency regulation, including latency, reliability, and security. Therefore, this paper focuses on providing a communication framework of interacting between the power grid management system and microgrid central controller. In this scenario, the microgrid control center is integrated into the utility grid as a frequency regulation supporter for the main grid. This communication structure emulates the information model of the IEC 61850 protocol to meet interoperability. By employing IoT's transmission protocol data distribution services, the structure satisfies the communication requirements for interacting in the wide-area network. This paper represents an interoperable information model for the microgrid central controller and power system management sectors' interactions based on the IEC 61850-8-2 standard. Furthermore, we evaluate our scenario by measuring the latency, reliability, and security performance of data distribution services on a real communication testbed.Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally control greenhouses' environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and financial decisions. It is thus important to accurately predict the crop yield in a greenhouse considering the benefits that can be brought by accurate greenhouse crop yield prediction. In this work, we have developed a new greenhouse crop yield prediction technique, by combining two state-of-the-arts networks for temporal sequence processing-temporal convolutional network (TCN) and recurrent neural network (RNN). Comprehensive evaluations of the proposed algorithm have been made on multiple datasets obtained from multiple real greenhouse sites for tomato growing. Based on a statistical analysis of the root mean square errors (RMSEs) between the predicted and actual crop yields, it is shown that the proposed approach achieves more accurate yield prediction performance than both traditional machine learning methods and other classical deep neural networks. Moreover, the experimental study also shows that the historical yield information is the most important factor for accurately predicting future crop yields.Synthesizing plans for a deformable object to transit from initial observations to goal observations, both of which are represented by high-dimensional data (namely "raw" data), is challenging due to the difficulty of learning abstract state representations of raw data and transition models of continuous states and continuous actions. Even though there have been some approaches making remarkable progress regarding the planning problem, they often neglect actions between observations and are unable to generate action sequences from initial observations to goal observations. In this paper, we propose a novel algorithm framework, namely AGN. We first learn a state-abstractor model to abstract states from raw observations, a state-generator model to generate raw observations from states, a heuristic model to predict actions to be executed in current states, and a transition model to transform current states to next states after executing specific actions. After that, we directly generate plans for a deformable object by performing the four models. We evaluate our approach in continuous domains and show that our approach is effective with comparison to state-of-the-art algorithms.Phenotypic characteristics of fruit particles, such as projection area, can reflect the growth status and physiological changes of grapes. However, complex backgrounds and overlaps always constrain accurate grape border recognition and detection of fruit particles. Therefore, this paper proposes a two-step phenotypic parameter measurement to calculate areas of overlapped grape particles. These two steps contain particle edge detection and contour fitting. For particle edge detection, an improved HED network is introduced. It makes full use of outputs of each convolutional layer, introduces Dice coefficients to original weighted cross-entropy loss function, and applies image pyramids to achieve multi-scale image edge detection. For contour fitting, an iterative least squares ellipse fitting and region growth algorithm is proposed to calculate the area of grapes. Experiments showed that in the edge detection step, compared with current prevalent methods including Canny, HED, and DeepEdge, the improved HED was able to extract the edges of detected fruit particles more clearly, accurately, and efficiently. It could also detect overlapping grape contours more completely. In the shape-fitting step, our method achieved an average error of 1.5% in grape area estimation. Therefore, this study provides convenient means and measures for extraction of grape phenotype characteristics and the grape growth law.The application of artificial intelligence techniques to wearable sensor data may facilitate accurate analysis outside of controlled laboratory settings-the holy grail for gait clinicians and sports scientists looking to bridge the lab to field divide. Using these techniques, parameters that are difficult to directly measure in-the-wild, may be predicted using surrogate lower resolution inputs. One example is the prediction of joint kinematics and kinetics based on inputs from inertial measurement unit (IMU) sensors. Tanespimycin mw Despite increased research, there is a paucity of information examining the most suitable artificial neural network (ANN) for predicting gait kinematics and kinetics from IMUs. This paper compares the performance of three commonly employed ANNs used to predict gait kinematics and kinetics multilayer perceptron (MLP); long short-term memory (LSTM); and convolutional neural networks (CNN). Overall high correlations between ground truth and predicted kinematic and kinetic data were found across all investigated ANNs. However, the optimal ANN should be based on the prediction task and the intended use-case application. For the prediction of joint angles, CNNs appear favourable, however these ANNs do not show an advantage over an MLP network for the prediction of joint moments. If real-time joint angle and joint moment prediction is desirable an LSTM network should be utilised.Neurosurgical resection represents an important therapeutic pillar in patients with brain metastasis (BM). Such extended treatment modalities require preoperative assessment of patients' physical status to estimate individual treatment success. The aim of the present study was to analyze the predictive value of frailty and sarcopenia as assessment tools for physiological integrity in patients with non-small cell lung cancer (NSCLC) who had undergone surgery for BM. Between 2013 and 2018, 141 patients were surgically treated for BM from NSCLC at the authors' institution. The preoperative physical condition was assessed by the temporal muscle thickness (TMT) as a surrogate parameter for sarcopenia and the modified frailty index (mFI). For the ≥65 aged group, median overall survival (mOS) significantly differed between patients classified as 'frail' (mFI ≥ 0.27) and 'least and moderately frail' (mFI less then 0.27) (15 months versus 11 months (p = 0.02)). Sarcopenia revealed significant differences in mOS for the less then 65 aged group (10 versus 18 months for patients with and without sarcopenia (p = 0.036)). The present study confirms a predictive value of preoperative frailty and sarcopenia with respect to OS in patients with NSCLC and surgically treated BM. A combined assessment of mFI and TMT allows the prediction of OS across all age groups.An important group of breast cancers is those associated with inherited susceptibility. In women, several predisposing mutations in genes involved in DNA repair have been discovered. Women with a germline pathogenic variant in BRCA1 have a lifetime cancer risk of 70%. As part of a larger prospective study on heavy metals, our aim was to investigate if blood arsenic levels are associated with breast cancer risk among women with inherited BRCA1 mutations. A total of 1084 participants with pathogenic variants in BRCA1 were enrolled in this study. Subjects were followed from 2011 to 2020 (mean follow-up time 3.75 years). During that time, 90 cancers were diagnosed, including 67 breast and 10 ovarian cancers. The group was stratified into two categories (lower and higher blood As levels), divided at the median ( less then 0.85 µg/L and ≥0.85 µg/L) As level among all unaffected participants. Cox proportional hazards models were used to model the association between As levels and cancer incidence. A high blood As level (≥0.85 µg/L) was associated with a significantly increased risk of developing breast cancer (HR = 2.05; 95%CI 1.18-3.56; p = 0.01) and of any cancer (HR = 1.73; 95%CI 1.09-2.74; p = 0.02). These findings suggest a possible role of environmental arsenic in the development of cancers among women with germline pathogenic variants in BRCA1.The forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have used Artificial Neural Networks (ANN) to forecast electricity demand, aiming to help newcomers and experienced researchers to appraise the common practices and to detect areas where there is room for improvement in the face of the current widespread deployment of smart meters and sensors, which yields an unprecedented amount of data to work with. The review looks at the specific problems tackled by each one of the selected papers, the results attained by their algorithms, and the strategies followed to validate and compare the results. This way, it is possible to highlight some peculiarities and algorithm configurations that seem to consistently outperform others in specific settings.